Tag: Conferences

Will give 7 presentations @APS DFD 24

Posted on by Jian-Xun Wang

We will be giving 7 talks in APS DFD meeting (from Sunday Nov 24 to Tuesday Nov 26 @Salt Lake City) https://meetings.aps.org/Meeting/DFD24/PersonIndex/4610. If you attend the meeting, please check them out.

  1. J.-X. Wang et al. CoNFiLD: Conditional Neural Field Latent Diffusion Model Generating Spatiotemporal Turbulence (10:50 AM, Sunday, November 24, 2024 Room: 255 E)
  2. P. Du et al. A Multi-Modal Implicit Neural Representation Method for Dimension Reduction of Spatiotemporal Flow Data (8:25 AM–10:40 AM, Monday, November 25, 2024
    Room: 155 B)
  3. X.-Y. Liu et al. Diff-FlowFSI: A GPU-accelerated, JAX-based Differentiable CFD Solver for Turbulent Flow and Fluid-Structure Interactions (9:05 AM–9:18 AM, Monday, November 25, 2024
    Room: 155 B)
  4. L. Sun et al. Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems (5:24 PM–5:37 PM)
  5. X. Fan et al. Generative AI for Synthesizing Spatio-temporal Wall Pressure Fluctuations in Turbulent Boundary Layers (1:03 PM–1:16 PM, Tuesday, November 26, 2024)
  6. Y. Liu et al. A DNS database of wall-pressure fluctuations beneath turbulent boundary layers with pressure gradients (1:29 PM–1:42 PM, Tuesday, November 26, 2024)
  7. M. H. Parikh, Generative Latent Diffusion Model for Stochastic Inflow Turbulence Synthesis (1:16 PM–1:29 PM, Tuesday, November 26, 2024)

Our group will give 3 presentations @SHTC, CA. July 15-17, 2024

Posted on by Jian-Xun Wang

J. Panda et al. Data-Driven Prediction of Thermal Field in Field-Effect Transistors Using Deep Neural Networks, Session K9-10, Wed. July 17, 2024, 3:35 PM – 5:15 PM

W. Shang et al. Physics-Integrated Hybrid Machine Learning Model for Phonon BTE,  Session K9-10, Wed. July 17, 2024, 3:35 PM – 5:15 PM

J. Zhou et al. Physics-Informed Neural Networks for Transistor Thermal Modeling Using Phonon Boltzmann Transport Equation, Session K9-09, Wed. July 17, 2024, 1:35 PM – 3:15 PM

https://event.asme.org/SHTC

Our group will give 6 presentations @ APS DFD 2023

Posted on by Jian-Xun Wang

Our group, CoMSAIL, will be delivering 6 presentations at APS DFD 2023. If you are also attending the conference in DC, please stop by and check out our talks (focusing on differentiable physics, GPU computing, and hybrid neural modeling for fluid flow)

J29.00003, Fan et al. Differentiable vectorized JAX solver for turbulent flow and fluid-structure interactions (4:35 PM–6:32 PM, Sunday, November 19, 2023, Session J 29, Room: 152B)

L17.00003 Sun et al. Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model (8:00 AM–10:36 AM, Monday, November 20,
Room: 145B)


L17.00005, Pan et al. Neural field based sequence model for generating spatial-temporal turbulence (8:00 AM–10:36 AM, Monday, November 20, 2023, Room: 145B)

X29.00001, Akhare et al. Implicit Neural Solver for Stable Surrogate Simulation of Fluid Dynamics (8:00 AM–10:36 AM, Tuesday, November 21, 2023, Room: 152B)

X29.00006, Liu et al. MuRFiV-Net: A Multi-Resolution Finite-Volume Inspired Neural Network for Predicting Spatiotemporal Dynamics (8:00 AM–10:36 AM, Tuesday, November 21, 2023, Room: 152B)

X29.00011, Zhang et al. A Differentiable Hybrid Neural Solver for Efficient Simulation of Cavitating Flows (8:00 AM–10:36 AM, Tuesday, November 21, 2023, Room: 152B)

Also one collaborative work with Prof. X. Zheng from RIT — R14.00007, Zheng et al. A hybrid physics informed neural network model for patient specific phonation simulation (1:50 PM–3:21 PM, Monday, November 20, 2023, Room: 144AB)

Presented PCML paper in USC Workshop on Research Challenges at the interface of Machine Learning and Uncertainty Quantification

Posted on by Jian-Xun Wang

Presented a paper entitled of Surrogate Modeling for Fluid Flows Based on Physics-Constrained, Label-Free Deep Learning at USC Workshop on Research Challenges at the interface of Machine Learning and Uncertainty Quantification. Please check out http://hyperion.usc.edu/MLUQ/agenda.html